Text Classification
Transformers
Safetensors
gemma4_unified
image-text-to-text
multimodal
merged
decision-model
Instructions to use akhilaaa3/Jev-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhilaaa3/Jev-Omni with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akhilaaa3/Jev-Omni")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("akhilaaa3/Jev-Omni") model = AutoModelForMultimodalLM.from_pretrained("akhilaaa3/Jev-Omni", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add decision-model tag (#2)
Browse files- Add decision-model tag (897e45119229bae80cdb2715c8eba830447a7d28)
Co-authored-by: Mishig ᠮᠢᠰᠾᠢᠭ <mishig@users.noreply.huggingface.co>
README.md
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base_model: google/gemma-4-12B-it
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library_name: transformers
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model_name: Jev-Omni
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tags: [text-classification, multimodal, merged]
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# Jev-Omni
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base_model: google/gemma-4-12B-it
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library_name: transformers
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model_name: Jev-Omni
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tags: [text-classification, multimodal, merged, decision-model]
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---
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# Jev-Omni
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